This document aims at exploring the dataset of 9 southern-individuals in 2014. For that purpose, we need first to load the weanlingNES package to load data.
# load library
library(weanlingNES)
# load data
data("data_ses", package = "weanlingNES")
# load("../data/data_ses.rda")
Let’s have a look at what’s inside data_ses$data_2014:
# list structure
str(data_ses$year_2014, max.level = 1, give.attr = F, no.list = T)
## $ ind_140059:Classes 'data.table' and 'data.frame': 2305 obs. of 45 variables:
## $ ind_140060:Classes 'data.table' and 'data.frame': 1866 obs. of 45 variables:
## $ ind_140062:Classes 'data.table' and 'data.frame': 2619 obs. of 45 variables:
## $ ind_140063:Classes 'data.table' and 'data.frame': 1570 obs. of 45 variables:
## $ ind_140068:Classes 'data.table' and 'data.frame': 1904 obs. of 45 variables:
## $ ind_140069:Classes 'data.table' and 'data.frame': 2123 obs. of 45 variables:
## $ ind_140072:Classes 'data.table' and 'data.frame': 2625 obs. of 45 variables:
## $ ind_140073:Classes 'data.table' and 'data.frame': 1692 obs. of 45 variables:
## $ ind_140075:Classes 'data.table' and 'data.frame': 1451 obs. of 45 variables:
A list of 9 data.frames, one for each seal
For convenience, we aggregate all 9 individuals into one dataset.
# combine all individuals
data_2014 <- rbindlist(data_ses$year_2014)
# display
DT::datatable(data_2014[sample.int(.N, 10), ], options = list(scrollX = T))
Table 1: Sample of 10 random rows from data_2014
Summary
# raw_data
data_2014[, .(
nb_days_recorded = uniqueN(as.Date(date)),
nb_dives = .N,
maxdepth_mean = mean(maxdepth),
dduration_mean = mean(dduration),
botttime_mean = mean(botttime),
pdi_mean = mean(pdi, na.rm = T)
), by = .id] %>%
sable(
caption = "Summary diving information relative to each 2018 individual",
digits = 2
)
Table 2: Summary diving information relative to each 2018 individual
|
.id
|
nb_days_recorded
|
nb_dives
|
maxdepth_mean
|
dduration_mean
|
botttime_mean
|
pdi_mean
|
|
ind_140059
|
238
|
2305
|
216.36
|
750.18
|
394.32
|
8218.73
|
|
ind_140060
|
189
|
1866
|
188.91
|
580.63
|
269.35
|
7903.56
|
|
ind_140062
|
264
|
2619
|
148.45
|
561.22
|
296.67
|
8643.92
|
|
ind_140063
|
159
|
1570
|
175.05
|
614.46
|
307.30
|
7779.18
|
|
ind_140068
|
192
|
1904
|
165.77
|
663.89
|
336.68
|
7950.71
|
|
ind_140069
|
212
|
2123
|
174.01
|
549.01
|
259.39
|
8052.52
|
|
ind_140072
|
264
|
2625
|
173.33
|
571.55
|
244.76
|
7928.25
|
|
ind_140073
|
176
|
1692
|
190.48
|
617.03
|
276.21
|
7850.67
|
|
ind_140075
|
141
|
1451
|
132.48
|
465.18
|
194.11
|
7837.99
|
Some explanatory plots
Missing values
# build dataset to check for missing values
dataPlot <- melt(data_2014[, .(.id, is.na(.SD)), .SDcol = -c(
".id",
"divenumber",
"year",
"month",
"day",
"hour",
"min",
"sec",
"juldate",
"divetype",
"date"
# "phase",
# "lat",
# "lon"
)])
# add the id of rows
dataPlot[, id_row := c(1:.N), by = c("variable", ".id")]
# plot
ggplot(dataPlot, aes(x = variable, y = id_row, fill = value)) +
geom_tile() +
labs(x = "Attributes", y = "Rows") +
scale_fill_manual(
values = c("white", "black"),
labels = c("Real", "Missing")
) +
facet_wrap(.id ~ ., scales = "free_y") +
theme_jjo() +
theme(
legend.position = "top",
axis.text.x = element_text(angle = 45, hjust = 1),
legend.key = element_rect(colour = "black")
)
Let’s look closer to the variables with missing values:
# table with percent
table_inter <- data_2014[, lapply(.SD, function(x) {
round(length(x[is.na(x)]) * 100 / length(x), 1)
}), .SDcol = -c(
".id",
"divenumber",
"year",
"month",
"day",
"hour",
"min",
"sec",
"juldate",
"divetype",
"date"
# "phase",
# "lat",
# "lon"
)]
# find which are different from 0
cond_inter <- sapply(table_inter, function(x) {
x == 0
})
# display the percentages that are over 0
table_inter[, which(cond_inter) := NULL] %>%
sable(caption = "Percentage of missing values per columns having missing values!") %>%
scroll_box(width = "100%")
Table 3: Percentage of missing values per columns having missing values!
|
driftrate
|
benthicdivevertrate
|
cornerindex
|
foragingindex
|
verticalspeed90perc
|
verticalspeed95perc
|
|
4.7
|
27
|
68.6
|
0.5
|
4.5
|
4.5
|
Nothing bad, missing values seem to occur only in column we are not interested in.
Outliers
Let’s see if we have some outliers. Some of them are quiet easy to spot looking at the distribution of dive duration:
# plot that, weirdly, doesn't free x axis...
# ggplot(
# melt(data_2014,
# id.vars = c(".id"),
# measure.vars = c("dduration","maxdepth","driftrate")),
# aes(x = value, fill = .id)
# ) +
# geom_histogram(show.legend = FALSE) +
# facet_grid(variable ~ .id,
# scales = "free"
# ) +
# labs(y = "# of dives") +
# theme_jjo()
ggplot(
melt(data_2014,
id.vars = c(".id"),
measure.vars = c("dduration")
),
aes(x = value, fill = .id)
) +
geom_histogram(show.legend = FALSE) +
facet_grid(variable ~ .id,
scales = "free"
) +
labs(y = "# of dives") +
theme_jjo() +
theme(
axis.title.x = element_blank(),
text = element_text(size = 8)
)
ggplot(
melt(data_2014,
id.vars = c(".id"),
measure.vars = c("maxdepth")
),
aes(x = value, fill = .id)
) +
geom_histogram(show.legend = FALSE) +
facet_grid(variable ~ .id,
scales = "free"
) +
scale_y_reverse() +
labs(y = "# of dives") +
theme_jjo() +
theme(
strip.text.x = element_blank(),
axis.title.x = element_blank(),
text = element_text(size = 8)
)
ggplot(
melt(data_2014,
id.vars = c(".id"),
measure.vars = c("driftrate")
),
aes(x = value, fill = .id)
) +
geom_histogram(show.legend = FALSE) +
facet_grid(variable ~ .id,
scales = "free"
) +
labs(y = "# of dives") +
theme_jjo() +
theme(
strip.text.x = element_blank(),
text = element_text(size = 8)
)
Nothing that obvious, which is great :)
All Variables
names_display <- names(data_2014[, -c(
".id",
"date",
"divenumber",
"year",
"month",
"day",
"hour",
"min",
"sec",
"juldate",
"divetype",
"day_departure",
"phase",
"lat",
"lon",
"sp"
)])
# calulate the median of driftrate for each day
median_driftrate <- data_2014[divetype == "2: drift",
.(driftrate = quantile(driftrate, 0.5)),
by = .(date = as.Date(date), .id)
]
# let's identity when the smooth changes sign
changes_driftrate <- median_driftrate %>%
.[, .(
y_smooth = predict(loess(driftrate ~ as.numeric(date), span = 0.25)),
date
), by = .id] %>%
.[c(FALSE, diff(sign(y_smooth)) != 0), ]
Full trip duration
for (i in names_display) {
cat("#####", i, "{.unlisted .unnumbered} \n")
if (i == "driftrate") {
print(
ggplot(
data = melt(data_2014[, .(.id, date, get(i), divetype)],
id.vars = c(".id", "date", "divetype")
),
aes(
x = as.Date(date),
y = value,
col = divetype
)
) +
geom_point(
alpha = 1 / 10,
size = .5
) +
geom_vline(
data = changes_driftrate,
aes(xintercept = date),
linetype = 2
) +
facet_wrap(. ~ .id, scales = "free") +
scale_x_date(date_labels = "%m/%Y") +
labs(x = "Date", y = "Drift Rate m/s", col = "Dive Type") +
theme_jjo() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
legend.position = "bottom"
) +
guides(colour = guide_legend(override.aes = list(
size = 7,
alpha = 1
)))
)
} else {
print(
ggplot(
data = melt(data_2014[, .(.id, date, get(i))],
id.vars = c(".id", "date")
),
aes(
x = as.Date(date),
y = value,
col = .id
)
) +
geom_point(
show.legend = FALSE,
alpha = 1 / 10,
size = .5
) +
geom_vline(
data = changes_driftrate,
aes(xintercept = date),
linetype = 2
) +
facet_wrap(. ~ .id, scales = "free") +
scale_x_date(date_labels = "%m/%Y") +
labs(x = "Date", y = i) +
theme_jjo() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
)
}
cat("\n \n")
}
The vertical dashed lines represent changes in buoyancy (see vignette("buoyancy_detect") for more information)
maxdepth

dduration

botttime

desctime

descrate

asctime

ascrate

pdi

dwigglesdesc

dwigglesbott

dwigglesasc

totvertdistbot

bottrange

efficiency

idz

driftdiveindex

driftrate

benthicdiveindex

benthicdivevertrate

cornerindex

foragingindex

verticalspeed90perc

verticalspeed95perc

dist_dep

temp

ssh

psu

vel

bathy

First month at sea
for (i in names_display) {
# subtitle
cat("#####", i, "{.unlisted .unnumbered} \n")
# print plot
print(
ggplot(
data = melt(data_2014[day_departure < 32,
.(.id, day_departure, get(i))],
id.vars = c(".id", "day_departure")),
aes(
x = day_departure,
y = value,
color = .id,
group = day_departure
)
) +
geom_boxplot(
show.legend = FALSE,
alpha = 1 / 10,
size = .5
) +
facet_wrap(. ~ .id, scales = "free") +
labs(x = "# days since departure", y = i) +
theme_jjo()
)
cat("\n \n")
}
maxdepth

dduration

botttime

desctime

descrate

asctime

ascrate

pdi

dwigglesdesc

dwigglesbott

dwigglesasc

totvertdistbot

bottrange

efficiency

idz

driftdiveindex

driftrate

benthicdiveindex

benthicdivevertrate

cornerindex

foragingindex

verticalspeed90perc

verticalspeed95perc

dist_dep

temp

ssh

psu

vel

bathy

Dive Type
# dataset to plot proportional area plot
data_2014[, sum_id := .N, by = .(.id, day_departure)] %>%
.[, sum_id_days := .N, by = .(.id, day_departure, divetype)] %>%
.[, prop := sum_id_days / sum_id]
dataPlot <- unique(data_2014[, .(prop, .id, divetype, day_departure)])
# area plot
ggplot(dataPlot, aes(
x = as.numeric(day_departure),
y = prop,
fill = as.character(divetype)
)) +
geom_area(alpha = 0.6, size = 1) +
facet_wrap(.id ~ ., scales = "free") +
theme_jjo() +
theme(legend.position = "bottom") +
labs(x = "# of days since departure",
y = "Proportion of dives",
fill = "Dive types")
Drift Rate
In the following graphs:
driftrate is calculated using only divetype == "2: drift"
- whereas all the others variables are calculated all dives considered
- Dives were
driftrate > 0 were excluded
# build dataset
dataPlot <- data_2014[divetype == "2: drift" &
driftrate < 0,
# median drift rate for drift dive
.(driftrate = median(driftrate, na.rm = T)),
by = .(.id, day_departure)] %>%
# merge to get other parameters including all dives
.[data_2014[driftrate < 0,
.(
# median dive duration all dives considered
dduration = median(dduration, na.rm = T),
# median max depth all dives considered
maxdepth = median(maxdepth, na.rm = T),
# median bottom dives all dives considered
botttime = median(botttime, na.rm = T)
),
by = .(.id, day_departure)],
on = c(".id", "day_departure")]
# plot
ggplot(dataPlot, aes(x = botttime, y = driftrate, col = .id)) +
geom_point(size = .5, alpha = .5) +
geom_smooth(method = "lm") +
guides(color = "none") +
facet_wrap(.id ~ .) +
scale_x_continuous(limits = c(0, 700)) +
labs(x = "Daily median Bottom time (s)",
y = "Daily median drift rate (m.s-1)") +
theme_jjo()
# plot
ggplot(dataPlot, aes(x = maxdepth, y = driftrate, col = .id)) +
geom_point(size = .5, alpha = .5) +
geom_smooth(method = "lm") +
guides(color = "none") +
scale_y_reverse() +
facet_wrap(.id ~ .) +
labs(x = "Daily median Maximum depth (m)",
y = "Daily median drift rate (m.s-1)") +
theme_jjo()
# plot
ggplot(dataPlot, aes(x = dduration, y = driftrate, col = .id)) +
geom_point(size = .5, alpha = .5) +
geom_smooth(method = "lm") +
guides(color = "none") +
facet_wrap(.id ~ .) +
labs(x = "Daily median Dive duration (s)",
y = "Daily median drift rate (m.s-1)") +
theme_jjo()
Behavioral Aerobic Dive Limit (bADL)
Based on Shero et al. (2018), we decided to look at the bADL as the 95th percentile of dive duration each day, for those with \(n \geq 8\). This threshold was chosen following this figure, please note that this number is particularly low cause only one dive every 2.2 hours was sampled:
ggplot(data_2014[,.(nb_dives = .N),
by = .(.id, day_departure)],
aes(x=nb_dives, fill=.id)) +
geom_histogram(show.legend = FALSE) +
facet_wrap(.~.id) +
labs(y="# of days", x = "# of dives per day") +
theme_jjo() +
theme(text = element_text(size = 8))
# select day that have at least 50 dives
days_to_keep = data_2014[,
.(nb_dives = .N),
by = .(.id, day_departure)] %>%
.[nb_dives >= 8,]
# keep only those days
data_2014_complete_day = merge(data_2014,
days_to_keep,
by = c(".id", "day_departure"))
# data plot
dataPlot = data_2014_complete_day[divetype=="1: foraging",
.(badl = quantile(dduration, 0.95)),
by = .(.id, day_departure)]
# combine two datasets to be able to use a second axis
# https://stackoverflow.com/questions/49185583/two-y-axes-with-different-scales-for-two-datasets-in-ggplot2
dataMegaPlot = rbind(data_2014_complete_day[divetype == "2: drift"] %>%
.[, .(w = .id,
y = driftrate,
x = day_departure,
z = "second_plot")],
dataPlot[, .(
w = .id,
# tricky one
y = (badl / 1000) - 1,
x = day_departure,
z = "first_plot"
)])
# plot
ggplot() +
geom_point(
data = dataMegaPlot[z == "second_plot", ],
aes(x = x, y = y),
alpha = 1 / 10,
size = 0.5,
color = "grey40",
show.legend = FALSE
) +
geom_path(data = dataMegaPlot[z == "first_plot", ],
aes(x = x, y = y, color = w),
show.legend = FALSE) +
scale_y_continuous(
# Features of the first axis
name = "Drift rate (m/s)",
# Add a second axis and specify its features
sec.axis = sec_axis( ~ (. * 1000) + 1000,
name = "Behavioral Aerobic Dive Limit (s)")
) +
labs(x = "# days since departure") +
facet_wrap(w ~ .) +
theme_jjo()
Looking at this graph, I want to believe that there is some kind of relationship between the bADL as defined by Shero et al. (2018) and the drift rate (and so buyoancy).
# get badl
dataplot_1 = data_2014_complete_day[,
.(badl = quantile(dduration, 0.95)),
by = .(.id, day_departure)]
# get driftrate
dataplot_2 = data_2014_complete_day[divetype == "2: drift",
.(driftrate = median(driftrate)),
by = .(.id, day_departure)]
# merge
dataPlot = merge(dataplot_1,
dataplot_2,
by = c(".id", "day_departure"),
all = TRUE)
# plot
ggplot(data = dataPlot[driftrate < 0, ],
aes(x = badl, y = driftrate, col = .id)) +
geom_point(show.legend = FALSE) +
geom_smooth(method = "lm", show.legend = FALSE) +
facet_wrap(.id~., scales = "free") +
labs(x = "Behavioral Aerobic Dive Limit (s)",
y = "Drift rate (m/s)") +
theme_jjo()

---
title: "Data Exploration SES - 2014"
author: "Joffrey JOUMAA"
date: "`r invisible(Sys.setlocale(locale = 'C')); format(Sys.Date(), format = '%B %d, %Y')`"
output:
  bookdown::html_document2:
    css: cosmo_custom.css
    number_sections: yes
    code_folding: show
    df_print: default
    fig_caption: yes
    code_download: yes
    toc: yes
    toc_float:
      collapsed: yes
      smooth_scroll: no
link-citations: yes
pkgdown:
  as_is: false
vignette: >
  %\VignetteIndexEntry{Data Exploration SES - 2014}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include=FALSE}
# command to build package without getting vignette error
# https://github.com/rstudio/renv/issues/833
# devtools::check(build_args=c("--no-build-vignettes"))

# reduce png size
knitr::knit_hooks$set(optipng = knitr::hook_optipng)
knitr::knit_hooks$set(pngquant = knitr::hook_pngquant)

# global option relative to rmarkdown
knitr::opts_chunk$set(
  cache = FALSE,
  echo = TRUE,
  fig.align = "center",
  out.width = "100%",
  message = FALSE,
  warning = FALSE,
  # tidy = TRUE,
  cache.lazy = FALSE,
  optipng = "-o7 -quiet",
  pngquant = "--speed=1"
)

# library
library(data.table)
library(magrittr)
library(kableExtra)

# remove some warnings
suppressWarnings(library(ggplot2))

# define my own table format: https://github.com/haozhu233/kableExtra/issues/374
sable <- function(x, escape = T, ...) {
  knitr::kable(x, escape = escape, ...) %>%
    kable_styling(
      bootstrap_options = c("striped", "hover", "responsive"),
      full_width = F
    )
}
```

This document aims at exploring the dataset of 9 southern-individuals in 2014. For that purpose, we need first to load the `weanlingNES` package to load data.

```{r data-exploration-2018-1}
# load library
library(weanlingNES)

# load data
data("data_ses", package = "weanlingNES")
# load("../data/data_ses.rda")
```

Let’s have a look at what’s inside `data_ses$data_2014`:
  
```{r data-exploration-2018-2}
# list structure
str(data_ses$year_2014, max.level = 1, give.attr = F, no.list = T)
```

> A list of `r length(data_ses$year_2014)` `data.frames`, one for each seal

For convenience, we aggregate all `r length(data_ses$year_2014)` individuals into one dataset.

```{r data-exploration-2018-3, eval=FALSE}
# combine all individuals
data_2014 <- rbindlist(data_ses$year_2014)

# display
DT::datatable(data_2014[sample.int(.N, 10), ], options = list(scrollX = T))
```
```{r data-exploration-2018-4, echo=FALSE, results='asis'}
# combine all individuals
data_2014 <- rbindlist(data_ses$year_2014)

# title
cat("<table style='width: 50%'>",
  paste0(
    "<caption>",
    "(#tab:myDThtmltools)",
    "Sample of 10 random rows from `data_2014`",
    "</caption>"
  ),
  "</table>",
  sep = "\n"
)

# display
DT::datatable(data_2014[sample.int(.N, 10), ], options = list(scrollX = T))
```

## Summary

```{r data-exploration-2018-5}
# raw_data
data_2014[, .(
  nb_days_recorded = uniqueN(as.Date(date)),
  nb_dives = .N,
  maxdepth_mean = mean(maxdepth),
  dduration_mean = mean(dduration),
  botttime_mean = mean(botttime),
  pdi_mean = mean(pdi, na.rm = T)
), by = .id] %>%
  sable(
    caption = "Summary diving information relative to each 2018 individual",
    digits = 2
  )
```

## Some explanatory plots

### Missing values

```{r data-exploration-2018-6, fig.cap="Check for missing value in 2018-individuals", fig.width=9}
# build dataset to check for missing values
dataPlot <- melt(data_2014[, .(.id, is.na(.SD)), .SDcol = -c(
  ".id",
  "divenumber",
  "year",
  "month",
  "day",
  "hour",
  "min",
  "sec",
  "juldate",
  "divetype",
  "date"
  # "phase",
  # "lat",
  # "lon"
)])
# add the id of rows
dataPlot[, id_row := c(1:.N), by = c("variable", ".id")]

# plot
ggplot(dataPlot, aes(x = variable, y = id_row, fill = value)) +
  geom_tile() +
  labs(x = "Attributes", y = "Rows") +
  scale_fill_manual(
    values = c("white", "black"),
    labels = c("Real", "Missing")
  ) +
  facet_wrap(.id ~ ., scales = "free_y") +
  theme_jjo() +
  theme(
    legend.position = "top",
    axis.text.x = element_text(angle = 45, hjust = 1),
    legend.key = element_rect(colour = "black")
  )
```

Let's look closer to the variables with missing values:

```{r}
# table with percent
table_inter <- data_2014[, lapply(.SD, function(x) {
  round(length(x[is.na(x)]) * 100 / length(x), 1)
}), .SDcol = -c(
  ".id",
  "divenumber",
  "year",
  "month",
  "day",
  "hour",
  "min",
  "sec",
  "juldate",
  "divetype",
  "date"
  # "phase",
  # "lat",
  # "lon"
)]

# find which are different from 0
cond_inter <- sapply(table_inter, function(x) {
  x == 0
})

# display the percentages that are over 0
table_inter[, which(cond_inter) := NULL] %>%
  sable(caption = "Percentage of missing values per columns having missing values!") %>%
  scroll_box(width = "100%")
```

Nothing bad, missing values seem to occur only in column we are not interested in.

### Outliers

Let’s see if we have some outliers. Some of them are quiet easy to spot looking at the distribution of dive duration:

```{r fig.cap="Distribution of `dduration`, `maxdepth` and `driftrate` for each seal", fig.show = "hold", fig.height=1.5}
# plot that, weirdly, doesn't free x axis...
# ggplot(
#   melt(data_2014,
#        id.vars = c(".id"),
#        measure.vars = c("dduration","maxdepth","driftrate")),
#   aes(x = value, fill = .id)
# ) +
#   geom_histogram(show.legend = FALSE) +
#   facet_grid(variable ~ .id,
#              scales = "free"
#   ) +
#   labs(y = "# of dives") +
#   theme_jjo()
ggplot(
  melt(data_2014,
    id.vars = c(".id"),
    measure.vars = c("dduration")
  ),
  aes(x = value, fill = .id)
) +
  geom_histogram(show.legend = FALSE) +
  facet_grid(variable ~ .id,
    scales = "free"
  ) +
  labs(y = "# of dives") +
  theme_jjo() +
  theme(
    axis.title.x = element_blank(),
    text = element_text(size = 8)
  )
ggplot(
  melt(data_2014,
    id.vars = c(".id"),
    measure.vars = c("maxdepth")
  ),
  aes(x = value, fill = .id)
) +
  geom_histogram(show.legend = FALSE) +
  facet_grid(variable ~ .id,
    scales = "free"
  ) +
  scale_y_reverse() +
  labs(y = "# of dives") +
  theme_jjo() +
  theme(
    strip.text.x = element_blank(),
    axis.title.x = element_blank(),
    text = element_text(size = 8)
  )
ggplot(
  melt(data_2014,
    id.vars = c(".id"),
    measure.vars = c("driftrate")
  ),
  aes(x = value, fill = .id)
) +
  geom_histogram(show.legend = FALSE) +
  facet_grid(variable ~ .id,
    scales = "free"
  ) +
  labs(y = "# of dives") +
  theme_jjo() +
  theme(
    strip.text.x = element_blank(),
    text = element_text(size = 8)
  )
```

Nothing that obvious, which is great :)

### All Variables

```{r}
names_display <- names(data_2014[, -c(
  ".id",
  "date",
  "divenumber",
  "year",
  "month",
  "day",
  "hour",
  "min",
  "sec",
  "juldate",
  "divetype",
  "day_departure",
  "phase",
  "lat",
  "lon",
  "sp"
)])

# calulate the median of driftrate for each day
median_driftrate <- data_2014[divetype == "2: drift",
  .(driftrate = quantile(driftrate, 0.5)),
  by = .(date = as.Date(date), .id)
]

# let's identity when the smooth changes sign
changes_driftrate <- median_driftrate %>%
  .[, .(
    y_smooth = predict(loess(driftrate ~ as.numeric(date), span = 0.25)),
    date
  ), by = .id] %>%
  .[c(FALSE, diff(sign(y_smooth)) != 0), ]
```

#### Full trip duration

```{r eval=FALSE, include=TRUE}
for (i in names_display) {
  cat("#####", i, "{.unlisted .unnumbered} \n")
  if (i == "driftrate") {
    print(
      ggplot(
        data = melt(data_2014[, .(.id, date, get(i), divetype)],
          id.vars = c(".id", "date", "divetype")
        ),
        aes(
          x = as.Date(date),
          y = value,
          col = divetype
        )
      ) +
        geom_point(
          alpha = 1 / 10,
          size = .5
        ) +
        geom_vline(
          data = changes_driftrate,
          aes(xintercept = date),
          linetype = 2
        ) +
        facet_wrap(. ~ .id, scales = "free") +
        scale_x_date(date_labels = "%m/%Y") +
        labs(x = "Date", y = "Drift Rate m/s", col = "Dive Type") +
        theme_jjo() +
        theme(
          axis.text.x = element_text(angle = 45, hjust = 1),
          legend.position = "bottom"
        ) +
        guides(colour = guide_legend(override.aes = list(
          size = 7,
          alpha = 1
        )))
    )
  } else {
    print(
      ggplot(
        data = melt(data_2014[, .(.id, date, get(i))],
          id.vars = c(".id", "date")
        ),
        aes(
          x = as.Date(date),
          y = value,
          col = .id
        )
      ) +
        geom_point(
          show.legend = FALSE,
          alpha = 1 / 10,
          size = .5
        ) +
        geom_vline(
          data = changes_driftrate,
          aes(xintercept = date),
          linetype = 2
        ) +
        facet_wrap(. ~ .id, scales = "free") +
        scale_x_date(date_labels = "%m/%Y") +
        labs(x = "Date", y = i) +
        theme_jjo() +
        theme(axis.text.x = element_text(angle = 45, hjust = 1))
    )
  }

  cat("\n \n")
}
```

The vertical dashed lines represent changes in buoyancy (see `vignette("buoyancy_detect")` for more information)

#### {.unlisted .unnumbered .tabset .tabset-fade .tabset-pills}

```{r results='asis', cache=TRUE, echo=FALSE, fig.height=7}
for (i in names_display) {
  cat("#####", i, "{.unlisted .unnumbered} \n")
  if (i == "driftrate") {
    print(
      ggplot(
        data = melt(data_2014[, .(.id, date, get(i), divetype)],
          id.vars = c(".id", "date", "divetype")
        ),
        aes(
          x = as.Date(date),
          y = value,
          col = divetype
        )
      ) +
        geom_point(
          alpha = 1 / 10,
          size = .5
        ) +
        geom_vline(
          data = changes_driftrate,
          aes(xintercept = date),
          linetype = 2
        ) +
        facet_wrap(. ~ .id, scales = "free") +
        scale_x_date(date_labels = "%m/%Y") +
        labs(x = "Date", y = "Drift Rate m/s", col = "Dive Type") +
        theme_jjo() +
        theme(
          axis.text.x = element_text(angle = 45, hjust = 1),
          legend.position = "bottom"
        ) +
        guides(colour = guide_legend(override.aes = list(
          size = 7,
          alpha = 1
        )))
    )
  } else {
    print(
      ggplot(
        data = melt(data_2014[, .(.id, date, get(i))],
          id.vars = c(".id", "date")
        ),
        aes(
          x = as.Date(date),
          y = value,
          col = .id
        )
      ) +
        geom_point(
          show.legend = FALSE,
          alpha = 1 / 10,
          size = .5
        ) +
        geom_vline(
          data = changes_driftrate,
          aes(xintercept = date),
          linetype = 2
        ) +
        facet_wrap(. ~ .id, scales = "free") +
        scale_x_date(date_labels = "%m/%Y") +
        labs(x = "Date", y = i) +
        theme_jjo() +
        theme(axis.text.x = element_text(angle = 45, hjust = 1))
    )
  }

  cat("\n \n")
}
```

#### First month at sea

```{r data-exploration-2018-18, eval=FALSE, include=TRUE}
for (i in names_display) {
  # subtitle
  cat("#####", i, "{.unlisted .unnumbered} \n")
  
  # print plot
  print(
      ggplot(
        data = melt(data_2014[day_departure < 32, 
                                     .(.id, day_departure, get(i))], 
                    id.vars = c(".id", "day_departure")),
        aes(
          x = day_departure,
          y = value,
          color = .id,
          group = day_departure
        )
      ) +
        geom_boxplot(
          show.legend = FALSE,
          alpha = 1 / 10,
          size = .5
        ) +
        facet_wrap(. ~ .id, scales = "free") +
        labs(x = "# days since departure", y = i) +
        theme_jjo()
    )
  cat("\n \n")
}
```

#### {.unlisted .unnumbered .tabset .tabset-fade .tabset-pills}

```{r data-exploration-2018-19, results='asis', cache=TRUE, echo=FALSE}
for (i in names_display) {
  # subtitle
  cat("#####", i, "{.unlisted .unnumbered} \n")
  
  # print plot
  print(
      ggplot(
        data = melt(data_2014[day_departure < 32, 
                                     .(.id, day_departure, get(i))], 
                    id.vars = c(".id", "day_departure")),
        aes(
          x = day_departure,
          y = value,
          color = .id,
          group = day_departure
        )
      ) +
        geom_boxplot(
          show.legend = FALSE,
          alpha = 1 / 10,
          size = .5
        ) +
        facet_wrap(. ~ .id, scales = "free") +
        labs(x = "# days since departure", y = i) +
        theme_jjo()
    )
  cat("\n \n")
}
```

## Dive Type

```{r fig.cap = "Evolution of dive type proportion"}
# dataset to plot proportional area plot
data_2014[, sum_id := .N, by = .(.id, day_departure)] %>%
  .[, sum_id_days := .N, by = .(.id, day_departure, divetype)] %>%
  .[, prop := sum_id_days / sum_id]
dataPlot <- unique(data_2014[, .(prop, .id, divetype, day_departure)])

# area plot
ggplot(dataPlot, aes(
  x = as.numeric(day_departure),
  y = prop,
  fill = as.character(divetype)
)) +
  geom_area(alpha = 0.6, size = 1) +
  facet_wrap(.id ~ ., scales = "free") +
  theme_jjo() +
  theme(legend.position = "bottom") +
  labs(x = "# of days since departure", 
       y = "Proportion of dives", 
       fill = "Dive types")
```

## Drift Rate

> In the following graphs:
>
> * `driftrate` is calculated using only `divetype == "2: drift"`
> * whereas all the others variables are calculated all dives considered
> * Dives were `driftrate > 0` were excluded

```{r data-exploration-2018-29}
# build dataset
dataPlot <- data_2014[divetype == "2: drift" &
                        driftrate < 0,
                      # median drift rate for drift dive
                      .(driftrate = median(driftrate, na.rm = T)),
                      by = .(.id, day_departure)] %>%
  # merge to get other parameters including all dives
  .[data_2014[driftrate < 0,
              .(
                # median dive duration all dives considered
                dduration = median(dduration, na.rm = T),
                # median max depth all dives considered
                maxdepth = median(maxdepth, na.rm = T),
                # median bottom dives all dives considered
                botttime = median(botttime, na.rm = T)
              ),
              by = .(.id, day_departure)],
    on = c(".id", "day_departure")]
```

```{r data-exploration-2018-30, fig.cap="Drift rate vs. Bottom time"}
# plot
ggplot(dataPlot, aes(x = botttime, y = driftrate, col = .id)) +
  geom_point(size = .5, alpha = .5) +
  geom_smooth(method = "lm") +
  guides(color = "none") +
  facet_wrap(.id ~ .) +
  scale_x_continuous(limits = c(0, 700)) +
  labs(x = "Daily median Bottom time (s)", 
       y = "Daily median drift rate (m.s-1)") +
  theme_jjo()
```

```{r data-exploration-2018-31, fig.cap="Drift rate vs. Maximum depth"}
# plot
ggplot(dataPlot, aes(x = maxdepth, y = driftrate, col = .id)) +
  geom_point(size = .5, alpha = .5) +
  geom_smooth(method = "lm") +
  guides(color = "none") +
  scale_y_reverse() +
  facet_wrap(.id ~ .) +
  labs(x = "Daily median Maximum depth (m)", 
       y = "Daily median drift rate (m.s-1)") +
  theme_jjo()
```

```{r data-exploration-2018-32, fig.cap="Drift rate vs. Dive duration"}
# plot
ggplot(dataPlot, aes(x = dduration, y = driftrate, col = .id)) +
  geom_point(size = .5, alpha = .5) +
  geom_smooth(method = "lm") +
  guides(color = "none") +
  facet_wrap(.id ~ .) +
  labs(x = "Daily median Dive duration (s)", 
       y = "Daily median drift rate (m.s-1)") +
  theme_jjo()
```

## Behavioral Aerobic Dive Limit (bADL)

Based on [Shero et *al.* (2018)](https://www.researchgate.net/publication/222683042_To_breathe_or_not_to_breathe_Optimal_breathing_aerobic_dive_limit_and_oxygen_stores_in_deep-diving_blue-eyed_shags), we decided to look at the *bADL* as the 95th percentile of dive duration each day, for those with $n \geq 8$. This threshold was chosen following this figure, please note that this number is particularly low cause only one dive every 2.2 hours was sampled:

```{r data-exploration-2018-36, fig.cap="Distribution of the number of dives each day. The threshold used to calculate bADL is fixed at 50 dives per day.", fig.height=3}
ggplot(data_2014[,.(nb_dives = .N), 
                        by = .(.id, day_departure)], 
       aes(x=nb_dives, fill=.id)) +
  geom_histogram(show.legend = FALSE) + 
  facet_wrap(.~.id) +
  labs(y="# of days", x = "# of dives per day") +
  theme_jjo() +
  theme(text = element_text(size = 8))
```

```{r data-exploration-2018-37, fig.cap="Behavioral ADL vs. drift rate along animals' trip (Am I the only one seeing some kind of relationship?)"}
# select day that have at least 50 dives
days_to_keep = data_2014[,
                                .(nb_dives = .N),
                                by = .(.id, day_departure)] %>%
  .[nb_dives >= 8,]

# keep only those days
data_2014_complete_day = merge(data_2014,
                                      days_to_keep,
                                      by = c(".id", "day_departure"))

# data plot
dataPlot = data_2014_complete_day[divetype=="1: foraging",
                                         .(badl = quantile(dduration, 0.95)),
                                         by = .(.id, day_departure)]

# combine two datasets to be able to use a second axis
# https://stackoverflow.com/questions/49185583/two-y-axes-with-different-scales-for-two-datasets-in-ggplot2
dataMegaPlot = rbind(data_2014_complete_day[divetype == "2: drift"] %>%
                       .[, .(w = .id,
                             y = driftrate,
                             x = day_departure,
                             z = "second_plot")],
                     dataPlot[, .(
                       w = .id,
                       # tricky one
                       y = (badl / 1000) - 1,
                       x = day_departure,
                       z = "first_plot"
                     )])

# plot
ggplot() +
  geom_point(
    data = dataMegaPlot[z == "second_plot", ],
    aes(x = x, y = y),
    alpha = 1 / 10,
    size = 0.5,
    color = "grey40",
    show.legend = FALSE
  ) +
  geom_path(data = dataMegaPlot[z == "first_plot", ],
            aes(x = x, y = y, color = w),
            show.legend = FALSE) +
  scale_y_continuous(
    # Features of the first axis
    name = "Drift rate (m/s)",
    # Add a second axis and specify its features
    sec.axis = sec_axis( ~ (. * 1000) + 1000, 
                         name = "Behavioral Aerobic Dive Limit (s)")
  ) +
  labs(x = "# days since departure") +
  facet_wrap(w ~ .) +
  theme_jjo()
```

> Looking at this graph, I want to believe that there is some kind of relationship between the *bADL* as defined by [Shero et *al.* (2018)](https://www.researchgate.net/publication/222683042_To_breathe_or_not_to_breathe_Optimal_breathing_aerobic_dive_limit_and_oxygen_stores_in_deep-diving_blue-eyed_shags) and the drift rate (and so buyoancy).

```{r data-exploration-2018-38}
# get badl
dataplot_1 = data_2014_complete_day[,
                              .(badl = quantile(dduration, 0.95)),
                              by = .(.id, day_departure)]
# get driftrate
dataplot_2 = data_2014_complete_day[divetype == "2: drift",
                              .(driftrate = median(driftrate)),
                              by = .(.id, day_departure)]

# merge
dataPlot = merge(dataplot_1,
                 dataplot_2,
                 by = c(".id", "day_departure"),
                 all = TRUE)

# plot
ggplot(data = dataPlot[driftrate < 0, ], 
       aes(x = badl, y = driftrate, col = .id)) +
  geom_point(show.legend = FALSE) +
  geom_smooth(method = "lm", show.legend = FALSE) +
  facet_wrap(.id~., scales = "free") +
  labs(x = "Behavioral Aerobic Dive Limit (s)",
       y = "Drift rate (m/s)") +
  theme_jjo()
```
